Evidence map›Paper›PMID 42523439›Full record

ArticlebioRxiv : the preprint server for biology2026

Regulatory T cells establish an IL-10-IL10Rα immunometabolic checkpoint that limits HSL activation and lipolysis.

Ramazan Yildiz, Kajal Davi, Niki F Brisnovali, James W R McMullen, Chung Hwan Cho, Khatanzul Ganbold, YoungUk Jang, Njeri Z R Sparman, Aidan Warnock, Gabriel Deards and 2 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Ramazan YildizDiabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
Kajal DaviDiabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
Niki F BrisnovaliDiabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
James W R McMullenDiabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
Chung Hwan ChoDiabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
Khatanzul GanboldDiabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
YoungUk JangDiabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
Njeri Z R SparmanDiabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
Aidan WarnockDiabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
Gabriel DeardsDiabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
Leigh GoedekeDiabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
Prashant RajbhandariDiabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029.

Funding

Decoding endocrine and paracrine communication through mammokinesDP1DK140003 · NIDDK · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Prashant Rajbhandari · 2024 to 2026
$2.4M
Interleukin-10 mediated immune cell-adipocyte crosstalk in adipose thermogenesisR01DK136035 · NIDDK · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Prashant Rajbhandari · 2024 to 2026
$1.9M
Cardiovascular Science Training Program (CSTP)T32HL176457 · NHLBI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Carol C Gregorio, Filip K Swirski · 2025 to 2026
$911k
Immune-adipocyte Crosstalk in Adipose-selective Thermogenic ProgramF32DK141191 · NIDDK · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Chung Hwan Cho · 2025 to 2026
$162k
NHLBI NIH HHS T32 HL176457NIDDK NIH HHS DP1 DK140003NIDDK NIH HHS F32 DK141191NIDDK NIH HHS R01 DK136035
6 · The paper itself

Abstract

Adipose tissue harbors a significant population of regulatory T (Treg) cells that enforce immune homeostasis, yet whether Tregs function as an immunometabolic checkpoint to directly regulate core adipocyte signaling programs remains incompletely defined. Here we show that adipose Tregs function as a dominant, time-dependent checkpoint on β-adrenergic signal-driven lipolytic program and signal transduction in adipocytes. Our integrated scRNA-seq, flow cytometry, and phosphoproteomics data show that prolonged adrenergic stimulation induces a progressive attenuation of activation of key lipase hormone-sensitive lipase (HSL) that coincides with Treg depletion in circulation and accumulation within white adipose tissue. Genetic perturbations establish Treg-derived interleukin-10 (IL-10) as the key mediator of this brake. IL-10 signaling through adipocyte IL-10Rα suppresses adrenergic HSL activation and rewires downstream signaling nodes that govern catecholamine responsiveness, lipolysis, and systemic energy homeostasis. Mechanistically, IL-10Rα engages a STAT3-dependent transcriptional program that induces the G-protein regulators RGS2 and RGS3, diminished PKA flux to HSL that reinforces suppression of the HSL activation state and lipolysis. Together, these findings define an adrenergic-immune feedback circuit in which Tregs fine tune the amplitude and duration of catecholamine responsiveness in adipocytes, establishing immune control of a core lipolytic pathway with implications for obesity-associated adipose dysfunction.

Identifiers

PMID42523439
PMCPMC13405277

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.